デジタルで「ブンガク」マイニング Tabata rev

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デジタルで「ブンガク」マイニング Tabata rev

  1. 1. LT@第15回 ARGカフェ デジタルで 「ブンガク」マイニングMining literariness in digital texts 田畑智司 大阪大学 GSLC
  2. 2. テクストマイニング
  3. 3. 「ブンガク」マイニング
  4. 4. Marley was dead: to begin with. There is no doubt whateverabout that. The register of his burial was signed by theclergyman, the clerk, the undertaker, and the chief mourner.Scrooge signed it. And Scrooges name was good upon Change,for anything he chose to put his hand to.Old Marley was as dead as a door-nail.Mind! I dont mean to say that I know, of my own knowledge,what there is particularly dead about a door-nail. I might havebeen inclined, myself, to regard a coffin-nail as the deadest pieceof ironmongery in the trade. But the wisdom of our ancestors isin the simile; and my unhallowed hands shall not disturb it, or theCountrys done for. You will therefore permit me to repeat,emphatically, that Marley was as dead as a door-nail. 4
  5. 5. Marley was dead: to begin with. There is no doubt whateverabout that. The register of his burial was signed by theclergyman, the clerk, the undertaker, and the chief mourner.Scrooge signed it. And Scrooges name was good upon Change,for anything he chose to put his hand to.Old Marley was as dead as a door-nail.Mind! I dont mean to say that I know, of my own knowledge,what there is particularly dead about a door-nail. I might havebeen inclined, myself, to regard a coffin-nail as the deadest pieceof ironmongery in the trade. But the wisdom of our ancestors isin the simile; and my unhallowed hands shall not disturb it, or theCountrys done for. You will therefore permit me to repeat,emphatically, that Marley was as dead as a door-nail. —Charles Dickens, A Christmas Carol (1843) 5
  6. 6. Function words(機能語)the, of, and, in, to, I, you, . . .l 高頻度で生起する単語l 頻度上位60項目のトークン数はテクストの総トークン数 (総語数)の約50%をカバーl テクストマイニングでも通常は stop words(ゴミ)として 排除(無視)される項目。いわんや,文学批評で取り上げ られることなど殆ど無い。 6
  7. 7. Function words(機能語)the, of, and, in, to, I, you, . . .l 高頻度で生起する単語l 頻度上位60項目のトークン数はテクストの総トークン数 (総語数)の約50%をカバーl テクストマイニングでも通常は stop words(ゴミ)として 排除(無視)される項目。いわんや,文学批評で取り上げ られることなど殆ど無い。 7
  8. 8. Function words(機能語)the, of, and, in, to, I, you, . . .l 高頻度で生起する単語l 頻度上位60項目のトークン数はテクストの総トークン数 (総語数)の約50%をカバーl テクストマイニングでも通常は stop words(ゴミ)として 排除(無視)される項目。いわんや,文学批評で取り上げ られることなど殆ど無い。 8
  9. 9. Function words(機能語)the, of, and, in, to, I, you, . . .l 高頻度で生起する単語l 頻度上位60項目のトークン数はテクストの総トークン数 (総語数)の約50%をカバーl テクストマイニングでも通常は stop words(ゴミ)として 排除(無視)される項目。いわんや,文学批評で取り上げ られることなど殆ど無い。これらの語はテクスト中で統 語的,文法的に存在するが,意味的,文学的には空気の ような無標のものでしかない。 9
  10. 10. これらの語はテクスト中で統語的,文法的に存在してるが,意味的,文学的には存在しないも同然である ... 10
  11. 11. これらの語はテクスト中で統語的,文法的に存在してるが,意味的,文学的には存在しないも同然である ... はたして本当にそうだろうか? 10
  12. 12. Table 1. Christmas Booksを構成する五作品Abbrev. Title Date Total of tokens Tokens in dialogueCarol A Christmas Carol 1843 28,420 7,917Chimes The Chimes 1844 30,805 13,240Cricket The Cricket on the Hearth 1845 31,832 11,627Battle The Battle of Life 1846 29,598 13,325Haunted The Haunted Man 1848 33,949 15,559 11
  13. 13. Table 2. Christmas Booksに登場する24人の主要人物の語彙頻度プロファイル I and the you is it a of not to(i) me in(p) my have to(p) do he be was that(d) what as for(p) her will(m)that(c) but are with yourScrooge 47.19 21.32 31.81 34.25 23.07 26.56 25.52 15.38 20.27 15.03 21.32 12.23 8.39 11.19 11.88 13.63 6.29 10.49 7.69 6.29 10.84 5.59 5.94 0.00 12.23 2.80 5.59 9.44 4.19 5.94Toby 45.52 29.06 28.36 17.86 28.71 30.46 21.01 13.31 18.56 10.50 10.85 10.15 14.36 10.50 11.56 8.05 2.45 8.05 7.70 3.15 7.00 8.40 5.95 11.56 5.95 4.55 5.25 9.80 4.90 3.50Meg 34.09 46.17 27.70 22.73 19.18 23.44 19.18 12.78 14.92 18.47 9.94 9.94 12.07 17.05 9.94 7.10 11.36 9.94 5.68 7.10 9.23 6.39 6.39 9.94 8.52 7.10 6.39 8.52 7.81 1.42Alderman 26.85 25.17 34.40 52.01 24.33 15.94 23.49 30.20 17.62 22.65 6.71 9.23 11.75 9.23 4.20 10.91 0.00 10.07 2.52 8.39 10.07 4.20 3.36 2.52 14.26 3.36 3.36 10.07 9.23 6.71Sir Joseph 27.96 37.53 45.62 18.40 22.08 8.09 23.55 28.70 10.30 20.60 9.57 16.92 20.60 13.98 11.77 6.62 12.51 13.25 1.47 6.62 3.68 8.09 6.62 0.00 11.77 8.83 2.94 2.21 14.72 10.30Will 43.71 29.68 25.36 18.89 21.59 18.89 28.60 9.17 18.35 14.57 19.43 13.49 13.49 9.17 11.33 9.17 5.40 7.02 5.40 8.64 3.78 11.33 9.17 4.32 7.56 2.70 9.71 3.24 10.79 8.10Dot 40.70 39.24 27.25 34.52 15.26 19.26 16.72 13.45 19.62 16.72 13.81 7.99 6.54 11.99 6.90 10.17 4.72 7.27 8.36 4.36 5.81 7.63 5.81 3.27 5.45 4.72 5.09 5.09 5.81 4.00John 53.09 28.89 25.28 20.95 24.20 25.28 23.11 11.92 13.72 14.45 14.09 11.20 11.92 11.56 6.86 6.14 14.81 7.95 9.39 7.95 5.42 7.22 7.95 18.78 7.95 8.67 6.14 4.70 6.50 1.45Caleb 44.53 24.17 31.81 25.45 24.17 24.81 20.36 11.45 19.72 20.36 13.36 12.72 17.18 20.36 10.18 8.27 8.91 8.91 7.00 7.00 10.18 9.54 14.63 14.00 3.18 3.18 10.18 4.45 6.36 1.91Tackleton 52.16 23.00 27.48 48.23 23.56 22.43 20.75 19.63 20.75 19.63 10.10 7.29 10.10 10.66 8.97 14.02 2.24 9.54 4.49 12.90 7.85 6.17 4.49 5.61 13.46 4.49 5.05 11.78 4.49 6.17Bertha 43.65 34.92 30.89 31.57 23.51 16.79 10.75 11.42 16.79 11.42 23.51 16.12 18.81 10.75 15.45 3.36 2.69 8.06 4.03 3.36 5.37 8.73 8.73 8.73 1.34 4.70 3.36 6.72 6.04 8.06Dr Jeddler 16.55 35.24 37.37 21.89 19.75 16.55 32.57 26.70 16.55 14.42 1.60 10.68 9.08 6.94 4.81 5.34 5.34 9.61 4.81 4.27 10.14 6.94 5.87 3.20 4.27 5.34 8.54 7.48 6.41 5.34Marion 57.96 35.98 14.66 23.32 7.33 15.99 8.66 15.32 15.32 12.66 19.32 12.66 16.66 8.00 14.66 11.99 9.33 8.00 11.33 8.00 3.33 4.00 8.66 5.33 4.00 14.66 11.33 2.00 4.66 4.66Clemency 37.72 29.63 18.86 30.17 22.09 21.01 16.70 15.63 22.63 18.86 11.32 7.54 3.77 8.08 8.62 11.85 11.85 9.70 17.24 9.16 6.47 7.00 7.00 17.78 4.85 7.00 5.39 3.23 4.85 2.16Snitchey 24.28 36.41 29.76 26.63 15.27 21.14 22.32 23.49 17.62 13.31 3.52 15.27 8.61 8.61 10.18 8.61 16.05 5.09 9.01 9.01 5.09 7.83 7.05 3.92 5.09 7.44 7.05 7.05 6.66 5.87Alfred 29.83 48.05 29.00 22.37 19.88 23.20 14.91 18.23 9.94 19.06 12.43 12.43 9.11 9.11 13.26 4.97 0.00 10.77 3.31 4.97 7.46 9.94 4.14 5.80 4.97 7.46 4.14 5.80 4.97 6.63Warden 54.13 35.64 21.78 25.74 15.18 18.48 15.84 21.78 16.50 19.80 11.88 21.78 14.52 11.22 10.56 10.56 3.30 6.60 3.30 10.56 5.28 7.26 7.92 5.28 7.26 5.28 7.26 2.64 11.22 5.28Redlaw 53.62 29.71 32.25 22.46 19.57 18.84 10.15 27.17 14.13 13.04 21.38 19.57 11.96 15.58 13.41 9.06 4.35 5.80 4.35 4.71 14.13 4.71 5.07 9.06 7.97 5.44 5.80 6.88 5.44 3.99William 21.74 33.75 25.74 21.74 36.61 26.89 22.31 25.17 11.44 16.02 2.86 13.73 8.01 8.58 6.87 4.01 4.58 6.87 5.15 18.88 16.02 8.01 5.15 10.87 2.29 2.29 6.29 9.15 3.43 1.72Milly 36.01 36.01 24.39 24.00 26.71 17.04 15.87 12.78 16.65 16.65 22.07 13.94 6.19 12.39 16.26 6.97 17.04 12.00 8.13 6.58 3.10 8.13 6.97 1.16 5.42 13.94 5.42 4.65 3.87 7.36Philip 39.14 37.08 23.17 12.36 22.14 21.63 19.57 15.45 12.36 17.51 10.81 10.30 25.23 8.75 8.24 7.72 8.24 4.12 16.99 7.21 4.12 11.33 8.24 5.15 2.58 5.66 6.18 3.61 6.18 2.58Mrs Tetterby 62.37 41.58 29.39 33.69 14.34 15.77 15.05 20.07 17.20 14.34 22.94 9.32 8.60 12.90 8.60 18.64 5.74 4.30 15.05 5.74 7.89 2.87 2.87 2.15 6.45 7.89 5.02 5.02 3.58 4.30Tetterby 43.88 25.48 27.60 36.80 20.52 22.65 16.99 21.23 19.82 15.57 9.20 11.32 29.02 11.32 9.20 14.15 0.00 4.25 7.79 7.08 11.32 7.08 4.25 2.83 9.20 7.08 3.54 8.49 9.91 19.11Ghost 30.51 34.97 43.90 17.86 21.58 14.14 18.60 31.99 8.93 9.67 8.19 24.55 23.07 10.42 8.19 3.72 5.21 7.44 8.19 1.49 3.72 7.44 2.23 5.95 3.72 2.23 5.95 1.49 4.46 2.23 this am there all know him if she his we here at when can has so(a.d.) or dear how would on(p) good no(det) had they come one very never beenScrooge 8.04 8.04 6.29 6.99 5.94 2.10 5.59 0.35 4.19 1.40 4.54 4.89 1.40 3.50 2.45 1.05 3.50 2.10 1.75 3.15 2.80 2.80 2.80 2.10 7.69 2.80 3.15 2.10 1.05 2.45Toby 3.85 8.05 6.30 4.20 7.00 1.40 5.95 7.70 1.75 15.76 13.31 3.85 3.15 3.50 1.40 1.40 5.60 3.15 2.45 3.15 4.20 4.20 2.80 2.80 4.55 3.15 4.55 3.50 4.20 1.75Meg 2.84 2.13 3.55 4.26 4.26 6.39 4.97 3.55 4.26 14.92 2.84 1.42 5.68 2.84 1.42 9.23 1.42 4.97 7.10 6.39 3.55 2.13 2.13 2.84 4.26 6.39 2.13 1.42 3.55 2.84Alderman 5.87 6.71 9.23 7.55 8.39 1.68 5.87 3.36 5.03 9.23 4.20 1.68 0.00 4.20 0.00 0.00 4.20 0.84 0.84 0.00 5.03 4.20 2.52 0.00 0.00 3.36 6.71 3.36 0.84 0.00Sir Joseph 8.83 5.89 3.68 2.21 0.74 5.15 4.42 0.00 10.30 2.94 2.94 7.36 2.21 3.68 4.42 2.21 2.21 1.47 1.47 2.94 4.42 4.42 5.89 0.74 1.47 0.00 1.47 3.68 0.00 2.21Will 6.48 10.25 6.48 1.62 1.62 7.56 2.70 1.62 3.78 5.94 2.70 4.32 10.79 7.56 2.70 1.62 8.64 0.00 3.24 2.16 3.24 3.24 2.16 1.08 5.40 1.08 4.86 1.08 2.16 0.00Dot 2.54 5.09 5.09 5.81 5.09 5.81 4.36 4.72 5.45 5.45 3.27 6.18 7.63 2.18 2.91 3.63 0.73 10.54 7.27 6.90 4.72 2.91 1.09 4.36 4.00 3.27 1.82 4.36 2.18 1.82John 4.70 3.25 5.78 2.53 4.70 6.50 4.33 13.00 3.25 4.33 3.25 5.42 2.17 3.61 5.06 2.17 2.89 1.81 5.06 4.33 2.53 2.17 2.53 6.50 0.36 2.89 1.45 4.70 4.33 2.17Caleb 1.91 3.18 5.73 6.36 6.36 5.09 5.09 6.36 2.55 1.91 3.82 9.54 1.91 0.00 2.55 2.55 3.82 5.09 5.09 6.36 5.73 4.45 1.91 3.18 3.18 1.27 3.18 2.55 5.73 5.73Tackleton 6.17 11.22 7.85 6.73 8.97 1.12 3.93 5.05 0.56 5.61 6.73 2.80 1.12 6.17 3.93 1.12 2.24 0.56 2.24 1.68 4.49 4.49 2.24 1.68 1.12 5.61 1.68 3.37 0.56 2.80Bertha 6.04 6.72 6.04 4.70 6.72 8.06 4.03 2.69 6.72 1.34 4.03 2.02 6.72 5.37 2.02 13.43 2.02 6.04 2.69 5.37 2.02 2.69 2.02 2.69 2.69 2.02 1.34 1.34 8.73 1.34Dr Jeddler 10.68 2.14 4.27 5.34 3.20 2.14 3.74 4.27 2.14 8.01 8.01 3.20 0.53 3.20 4.81 1.07 6.94 1.07 2.67 2.67 5.34 3.74 0.00 1.07 2.14 2.14 3.74 1.07 3.74 3.74Marion 4.66 4.00 7.33 7.33 3.33 14.66 6.00 2.00 7.33 0.67 4.00 2.00 5.33 4.66 3.33 8.00 4.00 6.00 2.67 6.66 4.66 1.33 3.33 7.33 0.67 0.67 4.00 2.00 9.99 1.33Clemency 6.47 8.08 3.77 5.93 9.16 5.93 6.47 14.01 2.69 0.54 4.31 3.23 5.39 1.08 4.31 1.62 1.08 3.23 6.47 6.47 2.69 3.77 2.69 5.39 5.39 3.23 4.31 4.85 2.69 2.16Snitchey 6.66 3.13 3.92 4.31 4.31 2.35 3.52 4.70 4.31 9.40 2.74 5.87 1.96 4.31 2.35 1.96 3.52 3.92 1.18 4.31 2.74 3.13 2.35 3.13 1.57 0.78 2.35 4.70 0.39 5.09Alfred 8.29 3.31 3.31 7.46 12.43 0.83 4.14 9.11 0.00 12.43 0.83 6.63 4.97 4.97 3.31 7.46 2.49 7.46 7.46 6.63 1.66 3.31 4.14 1.66 0.83 5.80 4.97 0.83 2.49 1.66Warden 4.62 9.24 3.30 3.30 9.90 1.98 6.60 8.58 6.60 0.66 3.30 1.98 2.64 3.30 2.64 1.32 4.62 0.00 1.98 4.62 2.64 1.32 7.26 1.32 1.32 1.98 4.62 1.32 2.64 1.32Redlaw 8.33 5.80 6.88 7.25 4.35 3.62 4.71 2.90 2.17 1.09 6.16 1.81 3.26 7.61 6.52 2.54 6.16 0.00 1.09 3.62 2.90 2.17 3.99 2.54 3.62 5.44 1.81 1.09 1.81 2.54William 4.01 2.86 5.15 6.29 4.58 5.15 7.44 5.15 4.58 3.43 2.29 6.87 4.01 1.72 5.15 3.43 1.72 2.86 2.29 4.01 2.29 2.86 2.86 1.14 2.86 3.43 3.43 1.14 3.43 2.86Milly 6.97 3.48 6.19 6.19 3.87 8.90 5.81 1.94 10.07 3.10 3.10 1.94 6.97 4.65 6.58 5.03 2.71 9.68 5.81 3.10 1.16 1.94 3.10 3.10 1.94 3.48 1.55 6.19 2.71 7.74Philip 4.12 7.21 5.15 5.66 4.12 7.21 2.06 3.09 6.69 1.55 4.63 4.63 6.18 1.55 4.63 6.18 4.63 1.55 1.03 0.52 3.09 5.15 1.55 3.09 4.63 4.63 3.09 3.09 1.55 1.55Mrs Tetterby 2.15 9.32 8.60 12.90 2.87 3.58 4.30 1.43 0.00 2.15 0.72 10.04 6.45 4.30 0.72 8.60 7.17 10.75 7.17 2.87 2.87 2.15 4.30 9.32 1.43 0.72 2.15 0.72 4.30 5.02Tetterby 2.12 3.54 5.66 6.37 4.25 2.83 8.49 4.95 1.42 1.42 3.54 3.54 2.12 3.54 4.25 0.71 4.25 6.37 2.83 1.42 1.42 5.66 2.83 5.66 1.42 4.95 0.71 3.54 1.42 4.95Ghost 9.67 4.46 5.21 5.95 0.00 3.72 3.72 3.72 5.95 1.49 5.21 1.49 2.98 0.74 2.98 0.74 3.72 1.49 2.23 6.70 5.21 1.49 8.93 5.95 1.49 1.49 2.98 0.74 0.00 2.23 12
  14. 14. I and the you is itScrooge 47.19 21.32 31.81 34.25 23.07 26.56Toby 45.52 29.06 28.36 17.86 28.71 30.46Meg 34.09 46.17 27.70 22.73 19.18 23.44Alderman 26.85 25.17 34.40 52.01 24.33 15.94Sir Joseph 27.96 37.53 45.62 18.40 22.08 8.09Will 43.71 29.68 25.36 18.89 21.59 18.89Dot 40.70 39.24 27.25 34.52 15.26 19.26John 53.09 28.89 25.28 20.95 24.20 25.28Caleb 44.53 24.17 31.81 25.45 24.17 24.81Tackleton 52.16 23.00 27.48 48.23 23.56 22.43Bertha 43.65 34.92 30.89 31.57 23.51 16.79Dr Jeddler 16.55 35.24 37.37 21.89 19.75 16.55Marion 57.96 35.98 14.66 23.32 7.33 15.99Clemency 37.72 29.63 18.86 30.17 22.09 21.0113
  15. 15. Table 2. Christmas Booksに登場する24人の主要人物の語彙頻度プロファイル I and the you is it a of not to(i) me in(p) my have to(p) do he be was that(d) what as for(p) her will(m)that(c) but are with yourScrooge 47.19 21.32 31.81 34.25 23.07 26.56 25.52 15.38 20.27 15.03 21.32 12.23 8.39 11.19 11.88 13.63 6.29 10.49 7.69 6.29 10.84 5.59 5.94 0.00 12.23 2.80 5.59 9.44 4.19 5.94Toby 45.52 29.06 28.36 17.86 28.71 30.46 21.01 13.31 18.56 10.50 10.85 10.15 14.36 10.50 11.56 8.05 2.45 8.05 7.70 3.15 7.00 8.40 5.95 11.56 5.95 4.55 5.25 9.80 4.90 3.50Meg 34.09 46.17 27.70 22.73 19.18 23.44 19.18 12.78 14.92 18.47 9.94 9.94 12.07 17.05 9.94 7.10 11.36 9.94 5.68 7.10 9.23 6.39 6.39 9.94 8.52 7.10 6.39 8.52 7.81 1.42Alderman 26.85 25.17 34.40 52.01 24.33 15.94 23.49 30.20 17.62 22.65 6.71 9.23 11.75 9.23 4.20 10.91 0.00 10.07 2.52 8.39 10.07 4.20 3.36 2.52 14.26 3.36 3.36 10.07 9.23 6.71Sir Joseph 27.96 37.53 45.62 18.40 22.08 8.09 23.55 28.70 10.30 20.60 9.57 16.92 20.60 13.98 11.77 6.62 12.51 13.25 1.47 6.62 3.68 8.09 6.62 0.00 11.77 8.83 2.94 2.21 14.72 10.30Will 43.71 29.68 25.36 18.89 21.59 18.89 28.60 9.17 18.35 14.57 19.43 13.49 13.49 9.17 11.33 9.17 5.40 7.02 5.40 8.64 3.78 11.33 9.17 4.32 7.56 2.70 9.71 3.24 10.79 8.10Dot 40.70 39.24 27.25 34.52 15.26 19.26 16.72 13.45 19.62 16.72 13.81 7.99 6.54 11.99 6.90 10.17 4.72 7.27 8.36 4.36 5.81 7.63 5.81 3.27 5.45 4.72 5.09 5.09 5.81 4.00John 53.09 28.89 25.28 20.95 24.20 25.28 23.11 11.92 13.72 14.45 14.09 11.20 11.92 11.56 6.86 6.14 14.81 7.95 9.39 7.95 5.42 7.22 7.95 18.78 7.95 8.67 6.14 4.70 6.50 1.45Caleb 44.53 24.17 31.81 25.45 24.17 24.81 20.36 11.45 19.72 20.36 13.36 12.72 17.18 20.36 10.18 8.27 8.91 8.91 7.00 7.00 10.18 9.54 14.63 14.00 3.18 3.18 10.18 4.45 6.36 1.91Tackleton 52.16 23.00 27.48 48.23 23.56 22.43 20.75 19.63 20.75 19.63 10.10 7.29 10.10 10.66 8.97 14.02 2.24 9.54 4.49 12.90 7.85 6.17 4.49 5.61 13.46 4.49 5.05 11.78 4.49 6.17Bertha 43.65 34.92 30.89 31.57 23.51 16.79 10.75 11.42 16.79 11.42 23.51 16.12 18.81 10.75 15.45 3.36 2.69 8.06 4.03 3.36 5.37 8.73 8.73 8.73 1.34 4.70 3.36 6.72 6.04 8.06Dr Jeddler 16.55 35.24 37.37 21.89 19.75 16.55 32.57 26.70 16.55 14.42 1.60 10.68 9.08 6.94 4.81 5.34 5.34 9.61 4.81 4.27 10.14 6.94 5.87 3.20 4.27 5.34 8.54 7.48 6.41 5.34Marion 57.96 35.98 14.66 23.32 7.33 15.99 8.66 15.32 15.32 12.66 19.32 12.66 16.66 8.00 14.66 11.99 9.33 8.00 11.33 8.00 3.33 4.00 8.66 5.33 4.00 14.66 11.33 2.00 4.66 4.66Clemency 37.72 29.63 18.86 30.17 22.09 21.01 16.70 15.63 22.63 18.86 11.32 7.54 3.77 8.08 8.62 11.85 11.85 9.70 17.24 9.16 6.47 7.00 7.00 17.78 4.85 7.00 5.39 3.23 4.85 2.16Snitchey 24.28 36.41 29.76 26.63 15.27 21.14 22.32 23.49 17.62 13.31 3.52 15.27 8.61 8.61 10.18 8.61 16.05 5.09 9.01 9.01 5.09 7.83 7.05 3.92 5.09 7.44 7.05 7.05 6.66 5.87Alfred 29.83 48.05 29.00 22.37 19.88 23.20 14.91 18.23 9.94 19.06 12.43 12.43 9.11 9.11 13.26 4.97 0.00 10.77 3.31 4.97 7.46 9.94 4.14 5.80 4.97 7.46 4.14 5.80 4.97 6.63Warden 54.13 35.64 21.78 25.74 15.18 18.48 15.84 21.78 16.50 19.80 11.88 21.78 14.52 11.22 10.56 10.56 3.30 6.60 3.30 10.56 5.28 7.26 7.92 5.28 7.26 5.28 7.26 2.64 11.22 5.28Redlaw 53.62 29.71 32.25 22.46 19.57 18.84 10.15 27.17 14.13 13.04 21.38 19.57 11.96 15.58 13.41 9.06 4.35 5.80 4.35 4.71 14.13 4.71 5.07 9.06 7.97 5.44 5.80 6.88 5.44 3.99William 21.74 33.75 25.74 21.74 36.61 26.89 22.31 25.17 11.44 16.02 2.86 13.73 8.01 8.58 6.87 4.01 4.58 6.87 5.15 18.88 16.02 8.01 5.15 10.87 2.29 2.29 6.29 9.15 3.43 1.72Milly 36.01 36.01 24.39 24.00 26.71 17.04 15.87 12.78 16.65 16.65 22.07 13.94 6.19 12.39 16.26 6.97 17.04 12.00 8.13 6.58 3.10 8.13 6.97 1.16 5.42 13.94 5.42 4.65 3.87 7.36Philip 39.14 37.08 23.17 12.36 22.14 21.63 19.57 15.45 12.36 17.51 10.81 10.30 25.23 8.75 8.24 7.72 8.24 4.12 16.99 7.21 4.12 11.33 8.24 5.15 2.58 5.66 6.18 3.61 6.18 2.58Mrs Tetterby 62.37 41.58 29.39 33.69 14.34 15.77 15.05 20.07 17.20 14.34 22.94 9.32 8.60 12.90 8.60 18.64 5.74 4.30 15.05 5.74 7.89 2.87 2.87 2.15 6.45 7.89 5.02 5.02 3.58 4.30Tetterby 43.88 25.48 27.60 36.80 20.52 22.65 16.99 21.23 19.82 15.57 9.20 11.32 29.02 11.32 9.20 14.15 0.00 4.25 7.79 7.08 11.32 7.08 4.25 2.83 9.20 7.08 3.54 8.49 9.91 19.11Ghost 30.51 34.97 43.90 17.86 21.58 14.14 18.60 31.99 8.93 9.67 8.19 24.55 23.07 10.42 8.19 3.72 5.21 7.44 8.19 1.49 3.72 7.44 2.23 5.95 3.72 2.23 5.95 1.49 4.46 2.23 this am there all know him if she his we here at when can has so(a.d.) or dear how would on(p) good no(det) had they come one very never beenScrooge 8.04 8.04 6.29 6.99 5.94 2.10 5.59 0.35 4.19 1.40 4.54 4.89 1.40 3.50 2.45 1.05 3.50 2.10 1.75 3.15 2.80 2.80 2.80 2.10 7.69 2.80 3.15 2.10 1.05 2.45Toby 3.85 8.05 6.30 4.20 7.00 1.40 5.95 7.70 1.75 15.76 13.31 3.85 3.15 3.50 1.40 1.40 5.60 3.15 2.45 3.15 4.20 4.20 2.80 2.80 4.55 3.15 4.55 3.50 4.20 1.75Meg 2.84 2.13 3.55 4.26 4.26 6.39 4.97 3.55 4.26 14.92 2.84 1.42 5.68 2.84 1.42 9.23 1.42 4.97 7.10 6.39 3.55 2.13 2.13 2.84 4.26 6.39 2.13 1.42 3.55 2.84Alderman 5.87 6.71 9.23 7.55 8.39 1.68 5.87 3.36 5.03 9.23 4.20 1.68 0.00 4.20 0.00 0.00 4.20 0.84 0.84 0.00 5.03 4.20 2.52 0.00 0.00 3.36 6.71 3.36 0.84 0.00Sir Joseph 8.83 5.89 3.68 2.21 0.74 5.15 4.42 0.00 10.30 2.94 2.94 7.36 2.21 3.68 4.42 2.21 2.21 1.47 1.47 2.94 4.42 4.42 5.89 0.74 1.47 0.00 1.47 3.68 0.00 2.21Will 6.48 10.25 6.48 1.62 1.62 7.56 2.70 1.62 3.78 5.94 2.70 4.32 10.79 7.56 2.70 1.62 8.64 0.00 3.24 2.16 3.24 3.24 2.16 1.08 5.40 1.08 4.86 1.08 2.16 0.00Dot 2.54 5.09 5.09 5.81 5.09 5.81 4.36 4.72 5.45 5.45 3.27 6.18 7.63 2.18 2.91 3.63 0.73 10.54 7.27 6.90 4.72 2.91 1.09 4.36 4.00 3.27 1.82 4.36 2.18 1.82John 4.70 3.25 5.78 2.53 4.70 6.50 4.33 13.00 3.25 4.33 3.25 5.42 2.17 3.61 5.06 2.17 2.89 1.81 5.06 4.33 2.53 2.17 2.53 6.50 0.36 2.89 1.45 4.70 4.33 2.17Caleb 1.91 3.18 5.73 6.36 6.36 5.09 5.09 6.36 2.55 1.91 3.82 9.54 1.91 0.00 2.55 2.55 3.82 5.09 5.09 6.36 5.73 4.45 1.91 3.18 3.18 1.27 3.18 2.55 5.73 5.73Tackleton 6.17 11.22 7.85 6.73 8.97 1.12 3.93 5.05 0.56 5.61 6.73 2.80 1.12 6.17 3.93 1.12 2.24 0.56 2.24 1.68 4.49 4.49 2.24 1.68 1.12 5.61 1.68 3.37 0.56 2.80Bertha 6.04 6.72 6.04 4.70 6.72 8.06 4.03 2.69 6.72 1.34 4.03 2.02 6.72 5.37 2.02 13.43 2.02 6.04 2.69 5.37 2.02 2.69 2.02 2.69 2.69 2.02 1.34 1.34 8.73 1.34Dr Jeddler 10.68 2.14 4.27 5.34 3.20 2.14 3.74 4.27 2.14 8.01 8.01 3.20 0.53 3.20 4.81 1.07 6.94 1.07 2.67 2.67 5.34 3.74 0.00 1.07 2.14 2.14 3.74 1.07 3.74 3.74Marion 4.66 4.00 7.33 7.33 3.33 14.66 6.00 2.00 7.33 0.67 4.00 2.00 5.33 4.66 3.33 8.00 4.00 6.00 2.67 6.66 4.66 1.33 3.33 7.33 0.67 0.67 4.00 2.00 9.99 1.33Clemency 6.47 8.08 3.77 5.93 9.16 5.93 6.47 14.01 2.69 0.54 4.31 3.23 5.39 1.08 4.31 1.62 1.08 3.23 6.47 6.47 2.69 3.77 2.69 5.39 5.39 3.23 4.31 4.85 2.69 2.16Snitchey 6.66 3.13 3.92 4.31 4.31 2.35 3.52 4.70 4.31 9.40 2.74 5.87 1.96 4.31 2.35 1.96 3.52 3.92 1.18 4.31 2.74 3.13 2.35 3.13 1.57 0.78 2.35 4.70 0.39 5.09Alfred 8.29 3.31 3.31 7.46 12.43 0.83 4.14 9.11 0.00 12.43 0.83 6.63 4.97 4.97 3.31 7.46 2.49 7.46 7.46 6.63 1.66 3.31 4.14 1.66 0.83 5.80 4.97 0.83 2.49 1.66Warden 4.62 9.24 3.30 3.30 9.90 1.98 6.60 8.58 6.60 0.66 3.30 1.98 2.64 3.30 2.64 1.32 4.62 0.00 1.98 4.62 2.64 1.32 7.26 1.32 1.32 1.98 4.62 1.32 2.64 1.32Redlaw 8.33 5.80 6.88 7.25 4.35 3.62 4.71 2.90 2.17 1.09 6.16 1.81 3.26 7.61 6.52 2.54 6.16 0.00 1.09 3.62 2.90 2.17 3.99 2.54 3.62 5.44 1.81 1.09 1.81 2.54William 4.01 2.86 5.15 6.29 4.58 5.15 7.44 5.15 4.58 3.43 2.29 6.87 4.01 1.72 5.15 3.43 1.72 2.86 2.29 4.01 2.29 2.86 2.86 1.14 2.86 3.43 3.43 1.14 3.43 2.86Milly 6.97 3.48 6.19 6.19 3.87 8.90 5.81 1.94 10.07 3.10 3.10 1.94 6.97 4.65 6.58 5.03 2.71 9.68 5.81 3.10 1.16 1.94 3.10 3.10 1.94 3.48 1.55 6.19 2.71 7.74Philip 4.12 7.21 5.15 5.66 4.12 7.21 2.06 3.09 6.69 1.55 4.63 4.63 6.18 1.55 4.63 6.18 4.63 1.55 1.03 0.52 3.09 5.15 1.55 3.09 4.63 4.63 3.09 3.09 1.55 1.55Mrs Tetterby 2.15 9.32 8.60 12.90 2.87 3.58 4.30 1.43 0.00 2.15 0.72 10.04 6.45 4.30 0.72 8.60 7.17 10.75 7.17 2.87 2.87 2.15 4.30 9.32 1.43 0.72 2.15 0.72 4.30 5.02Tetterby 2.12 3.54 5.66 6.37 4.25 2.83 8.49 4.95 1.42 1.42 3.54 3.54 2.12 3.54 4.25 0.71 4.25 6.37 2.83 1.42 1.42 5.66 2.83 5.66 1.42 4.95 0.71 3.54 1.42 4.95Ghost 9.67 4.46 5.21 5.95 0.00 3.72 3.72 3.72 5.95 1.49 5.21 1.49 2.98 0.74 2.98 0.74 3.72 1.49 2.23 6.70 5.21 1.49 8.93 5.95 1.49 1.49 2.98 0.74 0.00 2.23 14
  16. 16. Principal Components Analysis (PCA)主成分分析 (データ縮約手法の一つ) で データ中のパターンを視覚化 15
  17. 17. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 -6.0 É Ghost É Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 16
  18. 18. 0.3 not do are you it 0.2 what know all I there how dear come if was she 0.1 that(d) am had good very her me they never to(i) atPC 2 (11.84%) will(md) been so(a.d.) here we have when is one for 0 can that(c) or would your but a to(p) him on(p) be has he -0.1 as and my of with Frequency no(det) -0.2 the this his 0 20 40 in -0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 PC 1 (15.03%) Fig. 2 Christmas Booksの高頻度語60タイプの相互関係 17
  19. 19. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 -6.0 É Ghost É Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 18
  20. 20. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 19
  21. 21. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 19
  22. 22. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 20
  23. 23. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOT É É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA Dominant ÉÉ Submissive -2.0 Warden Will É 支配的 Dr Jeddler 順良 威圧的 献身的 -4.0 強権的 自己犠牲的 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 20
  24. 24. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 21
  25. 25. 6.0 対話的 口語的 4.0 庶民的 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 独白的 衒学的 ‘モンスター’ É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 21
  26. 26. Contrastive patterns in speech style(会話スタイルにおける対照的パターン) 22
  27. 27. Contrastive patterns in speech style(会話スタイルにおける対照的パターン) 23
  28. 28. 対話的 口語的 庶民的Dominant Submissive 支配的 順良 威圧的 献身的 強権的 自己犠牲的 独白的 衒学的 ‘モンスター’ 24
  29. 29. Social superiors Social underdogs Callousness Sympathy Cynicism OptimismCold-heartedness Fellow-feeling/love Stinginess Generosity Monsters Angels 25
  30. 30. Literary themes 26
  31. 31. Christmas Books の主題が高頻出語の生起パターンにも反映している 27
  32. 32. Stop words (ゴミ)としか思われていない素材にもInformation (宝)が埋もれている! 28
  33. 33. デジタルがもたらす新しい着眼点 29
  34. 34. Thank you @lctabata 30
  35. 35. Appendices 31
  36. 36. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX ÉPC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%) Fig. 1 Principal component scores for the 24 major characters: based on the 30 most common word-types in the corpus 32
  37. 37. 6.0 Interpersonal, vernacular 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOT É É Caleb Alderman MEGX ÉPC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Monologic, Ghost É X FEMALE CHARACTERS Latinate Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 Dominant PC 1 (15.04%) Submissive Fig. 1 Principal component scores for the 24 major characters: based on the 30 most common word-types in the corpus 32
  38. 38. 0.3 not do are you it 0.2 what know all I there how dear come if was she 0.1 that(d) am had good very her me they never to(i) atPC 2 (11.84%) will(md) been so(a.d.) here we have when is one for 0 can that(c) or would your but a to(p) him on(p) be has he -0.1 as and my of with Frequency no(det) -0.2 the this his 0 20 40 in -0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 PC 1 (15.03%) Fig. 2 Principal Components Analysis of the 30 most common word-types in the language of dialogue: based on the 24 major characters 33
  39. 39. 0.3 notInterpersonal, do vernacular are you it 0.2 what know all I there how dear come if was she 0.1 that(d) am had good very her me they never to(i) at PC 2 (11.84%) will(md) been so(a.d.) here we have when is one for 0 can that(c) or would your but a to(p) him on(p) be has he -0.1 as and my of with Frequency no(det) Monologic, -0.2 the Latinate this his 0 20 40 in -0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 Dominant PC 1 (15.03%) Submissive Fig. 2 Principal Components Analysis of the 30 most common word-types in the language of dialogue: based on the 24 major characters 33
  40. 40. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 34
  41. 41. 6.0 4.0 X É Mrs TETTERBY Tackleton X Tetterby É CLEMENCY 2.0 ÉÉ Toby Scrooge XDOTÉ É Caleb Alderman MEGX É PC 2 (11.84%) 0.0 É John X William É MARION É Alfred Redlaw É X XMILLY Snitchey É Philip BERTHA ÉÉ -2.0 Warden Will É Dr Jeddler -4.0 É Male characters -6.0 É Ghost É X FEMALE CHARACTERS Sir Joseph -8.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 8.0 PC 1 (15.04%)Fig. 1 Christmas Booksの主要登場人物24人の相互関係:高頻度語60タイプを変数として 34
  42. 42. 3.0 Tackleton Tetterby Alderman É É 2.0 Scrooge Mrs TETTERBY É É X BERTHA 1.0 Redlaw Caleb DOT XPC 2 (9.78%) X MARION É É X CLEMENCY X 0.0 Toby É Will John MILLY É Warden É Alfred ÉÉ MEG X William É X -1.0 Philip É Dr Jeddler Snitchey É Male characters -2.0 É É Ghost Sir Joseph É É X FEMALE CHARACTERS -3.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0 PC 1 (33.44%) Fig. 3 言語と性差:21項目の性差マーカーの生起頻度に基づく 35
  43. 43. 0.6 Frequency/1,000 you 0.5 0 20 40 0.4 will(md) 0.3 me 0.2 what dear good had PC 2 (9.8%) how never 0.1 so(a.d.) was 0 when is him -0.1 that(c) of would -0.2 a -0.3 the he and -0.4 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 PC 1 (33.4%)Fig. 4 Gender-oriented Difference in idiolects: Word plot for the 21 most common ‘marker’ words in the corpus 36
  44. 44. 140 E Male Characters X FEMALE CHARACTERS Scrooge#4 H 120 H Scrooge in 5 Parts Scrooge#5 H 100 Sir Joseph Scrooge#3 Alderman E H E Scrooge#2 H Caleb E PC 3 (12.97%) 80 Dr Jeddler Tackleton E EGhost E E Redlaw Toby E William E MILLY X Scrooge#1 Will E X BERTHA H 60 Alfred Tetterby EE EJohn Snitchey X MEG Warden E E X DOT 40 X CLEMENCY MRS TETTERBY X Philip E 20 MARION X 0 -60 -40 -20 0 20 40 60 80 PC 2 (16.22%)Fig. 5 A Christmas Carol における Scrooge 改心の軌跡: 頻度上位 20 タイプの生起頻度行列に基づく主成分分析の結果 37
  45. 45. 140 E Male Characters X FEMALE CHARACTERS Scrooge#4 H 120 H Scrooge in 5 Parts Scrooge#5 H 100 Sir Joseph Scrooge#3 Alderman E H E Scrooge#2 H Caleb E PC 3 (12.97%) 80 Dr Jeddler Tackleton E EGhost E E RedlawDominant William Toby E X E Submissive Scrooge#1 MILLY 支配的 H Will E X BERTHA 順良 60 Alfred 威圧的 Tetterby EE EJohn 献身的 Snitchey X MEG Warden 強権的 E X DOT E 自己犠牲的 40 X CLEMENCY MRS TETTERBY X Philip E 20 MARION X 0 -60 -40 -20 0 20 40 60 80 PC 2 (16.22%) Fig. 5 A Christmas Carol における Scrooge 改心の軌跡: 頻度上位 20 タイプの生起頻度行列に基づく主成分分析の結果 37
  46. 46. Important thematic contrasts like those betweensocial superiors and social inferiors, callousness versussympathy, cynicism versus family love and fellowfeeling, and so on, are underscored by thedifferentiation of idiolects or the variation within asingle idiolect. 38
  47. 47. Among the words lying to the LEFT in Fig 1 andcharacterise the idiolects of characters demonstratingdominance and habits of command, the first personpronoun you seems to play the most significant role,and other words, such as are and will, function inrelation to you or, to put it in another way, they are infrequent co-occurrence with you. 39
  48. 48. (1)A marked recourse to you know ‘It’s my place to give advice, you know, because I’m a Justice. You know I’m a Justice, don’t you?’ (Chimes: 172) ‘You will come to the wedding? We are in the same boat, you know’ (Cricket: 43). 40
  49. 49. (2) Assertive statements using you‘What right have you to be merry? what reason have you tobe merry? You’re poor enough.’ (Scrooge, Carol: 48); ‘After youare married, you’ll quarrel with your husband, and come to bea distressed wife. You may think not: but you will, because I tellyou so’ (Alderman, Chimes: 172). 41
  50. 50. (3) Impolite vocatives:Tackleton addresses John with ‘you dog!’ (Cricket: 45), Edwardwith ‘you vagabond’ (113), and Alderman Cute calls Richard ‘youdull dog’ (Chimes: 173), and ‘you silly fellow’ (173).These vocatives stand in marked contrast with more intimatevocatives, like ‘my dear’ or ‘my darling’ to which gentle andtender-hearted characters, such as Toby, Caleb, John, Philip, andso on, frequently resort. The differences in the use of vocativesindicate, with concise expressiveness, the relationships betweenthe characters as well as their attitude to each other. 42
  51. 51. (4) The sarcastic or taunting use of ‘you’. This type of usage frequently appears in Scrooge’s idiolect before he is haunted by the ghosts: (in the scene where his nephew invites Scrooge to the Christmas dinner) ‘What LEFT have you to be merry? what reason have you to be merry? You’re poor enough.’ (Carol: 48); in The Chimes it is often seen in the Alderman’s relentless bantering of Meg: ‘After you are married, you’ll quarrel with your husband, and come to be a distressed wife. You may think not: but you will, because I tell you so’ (Chimes: 172). 43

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